Technical analysis uses past prices and perhaps other past statistics to make investment decisions. Proponents of technical analysis believe that these data contain important information about future movements of the stock market. In practice, all major brokerage firms publish technical commentary on the market and many of the advisory services are based on technical analysis. In his interviews with them, Schwager (1993, 1995) finds that many top traders and fund managers use it. Moreover, Covel (2005), citing examples of large and successful hedge funds, advocates the use of technical analysis exclusively without learning any fundamental information on the market.
Academics, on the other hand, have long been skeptical about the usefulness of technical analysis, despite its widespread acceptance and adoption by practitioners. There are perhaps three reasons. The first reason is that there is no theoretical basis for it, which this paper attempts to provide. The second reason is that earlier theoretical studies often assume a random walk model for the stock price, which completely rules out any profitability from technical trading. The third reason is that earlier empirical findings, such as Cowles (1933) and Fama and Blume (1966), are mixed and inconclusive. Recently, however, Brock, Lakonishok, and LeBaron (1992), and especially Lo, Mamaysky, and Wang (2000), find strong evidence of profitability in technical trading based on more data and more elaborate strategies. These studies stimulated many subsequent academic research on technical analysis, but these later studies focus primarily on the statistical validity of the earlier results (reviewed in more detail in the next section).
In recent years, there has been an ever increasing trend to bring finance and insurance closer together. The motivation for the present paper could almost be quoted from Hans Bühlmann (1997) who in this context also coined the term “actuary of the third kind” in Bühlmann (1987): “ ... finance and insurance mathematics should be presented to today’s students as one discipline”. In accordance with this, our starting point is the observation that the valuation of random amounts is an important topic in both actuarial and financial mathematics and has been studied extensively in both fields. Almost any textbook includes a treatment under headings like premium principles or derivative pricing. We attempt to bring these approaches together by embedding an actuarial valuation principle in a financial environment.
The basic idea is simple. We begin with an a priori valuation rule which assigns a number (“premium”, “price”) to any random payoff from a suitable class. Typically, this rule is given or motivated by an actuarial premium principle. But the payoffs we consider do not exist in a vacuum; they are surrounded by a financial environment described by the outcomes of trades available to participants in a financial market. Such trades can be used to reduce the risk one has contracted by the sale or purchase of some random amount like an insurance claim or a financial obligation. To value a given payoff in this environment, we compare two procedures. One is to ignore the payoff completely and simply trade on the financial market in a subjectively optimal way. More precisely, one tries to obtain via trading from a given initial capital a final outcome with maximal value, where the value is computed according to the given a priori rule. Alternatively, one can first sell the payoff under consideration to increase one’s initial capital and then look for a trade whose resulting net final outcome (trading outcome minus payoff) has again maximal value. The selling price for the payoff is then defined implicitly by equating these two maximal values; it thus compensates for the payoff since one becomes indifferent between optimal trading alone and the combination of selling and optimal trading inclusive of the payoff. The resulting a posteriori valuation is called the financial transform of the a priori valuation rule.
‘Social capital’ figures in much development discourse as a mysterious substance, elusive yet vital, holding communities together, underpinning coping strategies, enabling entrepreneurship and thus forming one of the key conditions of possibility for poverty alleviation and poverty reduction strategies.
So central has this concept become that the definition of ‘social capital formation strategies’ has become a key requirement for government officials concerned with social development and poverty alleviation in South Africa (City of Cape Town, 2005). However, the phenomena usually lumped together under this ambiguous term are complex and multifaceted.
